The Experts below are selected from a list of 120 Experts worldwide ranked by ideXlab platform

Mark M. Mcgilchrist - One of the best experts on this subject based on the ideXlab platform.

  • possible sources of bias in primary care electronic health record Data use and reuse
    Journal of Medical Internet Research, 2018
    Co-Authors: Robert A. Verheij, Vasa Curcin, Brendan Delaney, Mark M. Mcgilchrist
    Abstract:

    Background: Enormous amounts of Data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these Data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for Data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. Objective: In this paper, we discuss whether Data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the Data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of Data. Methods: This paper is based on the authors’ experience as users of electronic health records Data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. Results: We first describe the different stages in the processing of electronic health record Data, as well as the different purposes for which these Data are used. Given the different Data processing steps and purposes, we then discuss the possible mechanisms for each individual Data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. Conclusions: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses Data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated Data collection process and environmental influences that can affect the quality of the Data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless Data quality issues are better understood and unless adequate controls are embedded throughout the Data Lifecycle, Data-driven health care will not live up to its expectations. We need a Data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda. [J Med Internet Res 2018;20(5):e185]

  • Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse
    Journal of medical Internet research, 2018
    Co-Authors: Robert A. Verheij, Brendan C. Delaney, Vasa Curcin, Mark M. Mcgilchrist
    Abstract:

    BACKGROUND Enormous amounts of Data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these Data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for Data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. OBJECTIVE In this paper, we discuss whether Data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the Data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of Data. METHODS This paper is based on the authors' experience as users of electronic health records Data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. RESULTS We first describe the different stages in the processing of electronic health record Data, as well as the different purposes for which these Data are used. Given the different Data processing steps and purposes, we then discuss the possible mechanisms for each individual Data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. CONCLUSIONS There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses Data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated Data collection process and environmental influences that can affect the quality of the Data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless Data quality issues are better understood and unless adequate controls are embedded throughout the Data Lifecycle, Data-driven health care will not live up to its expectations. We need a Data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Robert A. Verheij - One of the best experts on this subject based on the ideXlab platform.

  • possible sources of bias in primary care electronic health record Data use and reuse
    Journal of Medical Internet Research, 2018
    Co-Authors: Robert A. Verheij, Vasa Curcin, Brendan Delaney, Mark M. Mcgilchrist
    Abstract:

    Background: Enormous amounts of Data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these Data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for Data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. Objective: In this paper, we discuss whether Data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the Data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of Data. Methods: This paper is based on the authors’ experience as users of electronic health records Data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. Results: We first describe the different stages in the processing of electronic health record Data, as well as the different purposes for which these Data are used. Given the different Data processing steps and purposes, we then discuss the possible mechanisms for each individual Data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. Conclusions: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses Data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated Data collection process and environmental influences that can affect the quality of the Data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless Data quality issues are better understood and unless adequate controls are embedded throughout the Data Lifecycle, Data-driven health care will not live up to its expectations. We need a Data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda. [J Med Internet Res 2018;20(5):e185]

  • Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse
    Journal of medical Internet research, 2018
    Co-Authors: Robert A. Verheij, Brendan C. Delaney, Vasa Curcin, Mark M. Mcgilchrist
    Abstract:

    BACKGROUND Enormous amounts of Data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these Data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for Data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. OBJECTIVE In this paper, we discuss whether Data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the Data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of Data. METHODS This paper is based on the authors' experience as users of electronic health records Data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. RESULTS We first describe the different stages in the processing of electronic health record Data, as well as the different purposes for which these Data are used. Given the different Data processing steps and purposes, we then discuss the possible mechanisms for each individual Data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. CONCLUSIONS There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses Data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated Data collection process and environmental influences that can affect the quality of the Data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless Data quality issues are better understood and unless adequate controls are embedded throughout the Data Lifecycle, Data-driven health care will not live up to its expectations. We need a Data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Vasa Curcin - One of the best experts on this subject based on the ideXlab platform.

  • possible sources of bias in primary care electronic health record Data use and reuse
    Journal of Medical Internet Research, 2018
    Co-Authors: Robert A. Verheij, Vasa Curcin, Brendan Delaney, Mark M. Mcgilchrist
    Abstract:

    Background: Enormous amounts of Data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these Data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for Data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. Objective: In this paper, we discuss whether Data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the Data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of Data. Methods: This paper is based on the authors’ experience as users of electronic health records Data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. Results: We first describe the different stages in the processing of electronic health record Data, as well as the different purposes for which these Data are used. Given the different Data processing steps and purposes, we then discuss the possible mechanisms for each individual Data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. Conclusions: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses Data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated Data collection process and environmental influences that can affect the quality of the Data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless Data quality issues are better understood and unless adequate controls are embedded throughout the Data Lifecycle, Data-driven health care will not live up to its expectations. We need a Data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda. [J Med Internet Res 2018;20(5):e185]

  • Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse
    Journal of medical Internet research, 2018
    Co-Authors: Robert A. Verheij, Brendan C. Delaney, Vasa Curcin, Mark M. Mcgilchrist
    Abstract:

    BACKGROUND Enormous amounts of Data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these Data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for Data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. OBJECTIVE In this paper, we discuss whether Data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the Data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of Data. METHODS This paper is based on the authors' experience as users of electronic health records Data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. RESULTS We first describe the different stages in the processing of electronic health record Data, as well as the different purposes for which these Data are used. Given the different Data processing steps and purposes, we then discuss the possible mechanisms for each individual Data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. CONCLUSIONS There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses Data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated Data collection process and environmental influences that can affect the quality of the Data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless Data quality issues are better understood and unless adequate controls are embedded throughout the Data Lifecycle, Data-driven health care will not live up to its expectations. We need a Data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Wei Xu - One of the best experts on this subject based on the ideXlab platform.

  • nonparametric regression based failure rate model for electric power equipment using Lifecycle Data
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: Huifang Wang, Benteng He, Wanfang Zhao, Wei Xu
    Abstract:

    In order to analyze the fault trends more accurately, a failure rate model appropriate for general electric power equipment is established based on a nonparametric regression method, improved from stratified proportional hazards model (PHM), which can make maximum use of equipment Lifecycle Data as the covariates, including manufacturer, service age, location, maintainer, health index, etc. All of covariates are represented in the hierarchy process of equipment health condition, which is beneficial for processing and classifying the Lifecycle Data into multitype recurrent events quantitatively. Meanwhile, based on new definitions of single health cycle and time between events, recurrent inspecting events distributed with martingale process can correspond with event-specific failure function during equipment Lifecycle. On this occasion, more inspecting events can be utilized in a complete cycle to predict potential risk and assess equipment health condition. Then, stratified nonparametric PHM is employed to build the multitype recurrent events-specific failure model appropriate for competing risk problem toward interval censored. Lastly, the example in terms of transformers demonstrates the modeling procedure. Results show the well asymptotic property and goodness-of-fit tested by both of graphical and analytical methods. Compared with existing failure models, such as age-based or CBF model, this improved nonparametric regression model can mine Lifecycle Data acquisition from asset management system, depict the failure trend accurately considering both individual and group features, and lay the foundation for health prognosis, maintenance optimization, and asset management in power grid.

Huifang Wang - One of the best experts on this subject based on the ideXlab platform.

  • nonparametric regression based failure rate model for electric power equipment using Lifecycle Data
    IEEE PES Transmission and Distribution Conference and Exposition, 2016
    Co-Authors: Huifang Wang, Benteng He
    Abstract:

    In order to analyze the fault trends more accurately, a failure rate model appropriate for general electric power equipment is established based on a nonparametric regression method, improved from stratified proportional hazards model (PHM), which can make maximum use of equipment Lifecycle Data as the covariates, including manufacturer, service age, location, maintainer, health index, etc. All of covariates are represented in the hierarchy process of equipment health condition, which is beneficial for processing and classifying the Lifecycle Data into multitype recurrent events quantitatively. On this occasion, more inspecting events can be utilized in a complete cycle to predict potential risk and assess equipment health condition. Then, stratified nonparametric PHM is employed to build the multitype recurrent events-specific failure model appropriate for competing risk problem toward interval censored. Lastly, the example in terms of transformers demonstrates the modeling procedure. Results show the well asymptotic property and goodness-of-fit tested by both of graphical and analytical methods. Compared with existing failure models, such as age-based or CBF model, this improved nonparametric regression model can mine Lifecycle Data acquisition from asset management system, depict the failure trend accurately considering both individual and group features, and lay the foundation for health prognosis, maintenance optimization, and asset management in power grid.

  • nonparametric regression based failure rate model for electric power equipment using Lifecycle Data
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: Huifang Wang, Benteng He, Wanfang Zhao, Wei Xu
    Abstract:

    In order to analyze the fault trends more accurately, a failure rate model appropriate for general electric power equipment is established based on a nonparametric regression method, improved from stratified proportional hazards model (PHM), which can make maximum use of equipment Lifecycle Data as the covariates, including manufacturer, service age, location, maintainer, health index, etc. All of covariates are represented in the hierarchy process of equipment health condition, which is beneficial for processing and classifying the Lifecycle Data into multitype recurrent events quantitatively. Meanwhile, based on new definitions of single health cycle and time between events, recurrent inspecting events distributed with martingale process can correspond with event-specific failure function during equipment Lifecycle. On this occasion, more inspecting events can be utilized in a complete cycle to predict potential risk and assess equipment health condition. Then, stratified nonparametric PHM is employed to build the multitype recurrent events-specific failure model appropriate for competing risk problem toward interval censored. Lastly, the example in terms of transformers demonstrates the modeling procedure. Results show the well asymptotic property and goodness-of-fit tested by both of graphical and analytical methods. Compared with existing failure models, such as age-based or CBF model, this improved nonparametric regression model can mine Lifecycle Data acquisition from asset management system, depict the failure trend accurately considering both individual and group features, and lay the foundation for health prognosis, maintenance optimization, and asset management in power grid.